Clinician-Deployable Deep Hypergraph Model Integrating Clinical and CT Radiomics Predicts Immunotherapy Outcomes in NSCLC
Description
The dataset consists of pre-treatment CT images from non-small cell lung cancer (NSCLC) patients who received immunotherapy, provided by Memorial Sloan Kettering Cancer Center. Through searching the source data, we identified 136 patients with complete CT images and corresponding clinical information. We provide CT image data in nii.gz format along with manually segmented lesion masks delineated by the radiologists. All open-access data in this repository are consistent with the original source data. The source data are cited as follows: Vanguri RS, Luo J, Aukerman AT, Egger JV, Fong CJ, Horvat N, Pagano A, Araujo-Filho JAB, Geneslaw L, Rizvi H, Sosa R, Boehm KM, Yang SR, Bodd FM, Ventura K, Hollmann TJ, Ginsberg MS, Gao J; MSK MIND Consortium; Hellmann MD, Sauter JL, Shah SP. Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L)1 blockade in patients with non-small cell lung cancer. Nat Cancer. 2022 Oct;3(10):1151-1164.
Files
Steps to reproduce
Please refer to this study provides step by step instructions: https://github.com/JD910/DHGN
Institutions
- China Medical University